Sistemas reactivos en Java

Programación

1. Introducción

En este tutorial, comprenderemos los conceptos básicos de la creación de sistemas reactivos en Java utilizando Spring y otras herramientas y marcos.

En el proceso, discutiremos cómo la programación reactiva es solo un impulsor hacia la creación de un sistema reactivo. Esto nos ayudará a comprender la razón fundamental para crear sistemas reactivos y diferentes especificaciones, bibliotecas y estándares que ha inspirado a lo largo del camino.

2. ¿Qué son los sistemas reactivos?

Durante las últimas décadas, el panorama tecnológico ha experimentado varias interrupciones que han llevado a una transformación completa en la forma en que vemos el valor de la tecnología. El mundo de la informática antes de Internet nunca podría haber imaginado las formas y los medios en los que cambiará nuestro día actual.

Con el alcance de Internet a las masas y la experiencia en constante evolución que promete, los arquitectos de aplicaciones deben estar alerta para satisfacer su demanda.

Básicamente, esto significa que nunca podremos diseñar una aplicación como solíamos hacerlo antes. Una aplicación altamente receptiva ya no es un lujo sino una necesidad .

Eso también ocurre frente a fallas aleatorias y cargas impredecibles. ¡La necesidad de la hora no es solo obtener el resultado correcto sino obtenerlo rápidamente! Es muy importante impulsar las increíbles experiencias de usuario que prometemos ofrecer.

Esto es lo que crea la necesidad de un estilo arquitectónico que nos pueda brindar Sistemas Reactivos.

2.1. Manifiesto reactivo

En el año 2013, un equipo de desarrolladores, liderado por Jonas Boner, se reunió para definir un conjunto de principios básicos en un documento conocido como Manifiesto Reactivo. Esto es lo que sentó las bases de un estilo de arquitectura para crear sistemas reactivos. Desde entonces, este manifiesto ha despertado mucho interés en la comunidad de desarrolladores.

Básicamente, este documento prescribe la receta para que un sistema reactivo sea flexible, poco acoplado y escalable . Esto hace que dichos sistemas sean fáciles de desarrollar, tolerantes a fallas y, lo que es más importante, altamente receptivos, la base para experiencias de usuario increíbles.

Entonces, ¿cuál es esta receta secreta? Bueno, ¡no es ningún secreto! El manifiesto define las características o principios fundamentales de un sistema reactivo:

  • Responsive : Un sistema reactivo debe proporcionar un tiempo de respuesta rápido y constante y, por lo tanto, una calidad de servicio constante.
  • Resiliente : un sistema reactivo debe seguir respondiendo en caso de fallas aleatorias mediante la replicación y el aislamiento
  • Elástico : un sistema de este tipo debe seguir respondiendo bajo cargas de trabajo impredecibles a través de una escalabilidad rentable
  • Basado en mensajes : debe basarse en el paso de mensajes asincrónicos entre los componentes del sistema

Estos principios parecen sencillos y sensatos, pero no siempre son más fáciles de implementar en una arquitectura empresarial compleja. En este tutorial, desarrollaremos un sistema de muestra en Java con estos principios en mente.

3. ¿Qué es la programación reactiva?

Antes de continuar, es importante comprender la diferencia entre la programación reactiva y los sistemas reactivos. Usamos ambos términos con bastante frecuencia y fácilmente malinterpretamos uno por el otro. Como hemos visto anteriormente, los sistemas reactivos son el resultado de un estilo arquitectónico específico.

En contraste, la programación reactiva es un paradigma de programación donde el foco está en desarrollar componentes asincrónicos y sin bloqueo . El núcleo de la programación reactiva es un flujo de datos que podemos observar y reaccionar, incluso aplicar contrapresión también. Esto conduce a una ejecución sin bloqueo y, por lo tanto, a una mejor escalabilidad con menos subprocesos de ejecución.

Ahora bien, esto no significa que los sistemas reactivos y la programación reactiva sean mutuamente excluyentes. De hecho, la programación reactiva es un paso importante hacia la realización de un sistema reactivo, ¡pero no lo es todo!

3.1. Flujos reactivos

Reactive Streams es una iniciativa comunitaria que comenzó en el año 2013 para proporcionar un estándar para el procesamiento de flujo asíncrono con contrapresión sin bloqueo . El objetivo aquí era definir un conjunto de interfaces, métodos y protocolos que puedan describir las operaciones y entidades necesarias.

Desde entonces, han surgido varias implementaciones en múltiples lenguajes de programación que cumplen con la especificación de flujos reactivos. Estos incluyen Akka Streams, Ratpack y Vert.x, por nombrar algunos.

3.2. Bibliotecas reactivas para Java

Uno de los objetivos iniciales detrás de los flujos reactivos fue eventualmente incluirse como una biblioteca estándar oficial de Java. Como resultado, la especificación de flujos reactivos es semánticamente equivalente a la biblioteca Java Flow, introducida en Java 9.

Aparte de eso, hay algunas opciones populares para implementar la programación reactiva en Java:

  • Extensiones reactivas: conocidas popularmente como ReactiveX, proporcionan API para programación asincrónica con flujos observables. Estos están disponibles para múltiples lenguajes de programación y plataformas, incluido Java, donde se conoce como RxJava.
  • Project Reactor: esta es otra biblioteca reactiva, puesta a tierra basada en la especificación de flujos reactivos, destinada a construir no aplicaciones en la JVM. También resulta ser la base de la pila reactiva en el ecosistema de Spring.

4. Una aplicación sencilla

Para el propósito de este tutorial, desarrollaremos una aplicación simple basada en arquitectura de microservicios con una interfaz mínima. La arquitectura de la aplicación debe tener suficientes elementos para crear un sistema reactivo.

Para nuestra aplicación, adoptaremos programación reactiva de extremo a extremo y otros patrones y herramientas para lograr las características fundamentales de un sistema reactivo.

4.1. Arquitectura

Comenzaremos por definir una arquitectura de aplicación simple que no necesariamente exhibe las características de los sistemas reactivos . A partir de ahí, realizaremos los cambios necesarios para conseguir estas características una a una.

Entonces, primero, comencemos por definir una arquitectura simple:

This is quite a simple architecture that has a bunch of microservices to facilitate a commerce use-case where we can place an order. It also has a frontend for user experience, and all communication happens as REST over HTTP. Moreover, every microservice manages their data in individual databases, a practice known as database-per-service.

We'll go ahead and create this simple application in the following sub-sections. This will be our base to understand the fallacies of this architecture and ways and means to adopt principles and practices so that we can transform this into a reactive system.

4.3. Inventory Microservice

Inventory microservice will be responsible for managing a list of products and their current stock. It will also allow altering the stock as orders are processed. We'll use Spring Boot with MongoDB to develop this service.

Let's begin by defining a controller to expose some endpoints:

@GetMapping public List getAllProducts() { return productService.getProducts(); } @PostMapping public Order processOrder(@RequestBody Order order) { return productService.handleOrder(order); } @DeleteMapping public Order revertOrder(@RequestBody Order order) { return productService.revertOrder(order); }

and a service to encapsulate our business logic:

@Transactional public Order handleOrder(Order order) { order.getLineItems() .forEach(l -> { Product> p = productRepository.findById(l.getProductId()) .orElseThrow(() -> new RuntimeException("Could not find the product: " + l.getProductId())); if (p.getStock() >= l.getQuantity()) { p.setStock(p.getStock() - l.getQuantity()); productRepository.save(p); } else { throw new RuntimeException("Product is out of stock: " + l.getProductId()); } }); return order.setOrderStatus(OrderStatus.SUCCESS); } @Transactional public Order revertOrder(Order order) { order.getLineItems() .forEach(l -> { Product p = productRepository.findById(l.getProductId()) .orElseThrow(() -> new RuntimeException("Could not find the product: " + l.getProductId())); p.setStock(p.getStock() + l.getQuantity()); productRepository.save(p); }); return order.setOrderStatus(OrderStatus.SUCCESS); }

Note that we're persisting the entities within a transaction, which ensures that no inconsistent state results in case of exceptions.

Apart from these, we'll also have to define the domain entities, the repository interface, and a bunch of configuration classes necessary for everything to work properly.

But since these are mostly boilerplate, we'll avoid going through them, and they can be referred to in the GitHub repository provided in the last section of this article.

4.4. Shipping Microservice

The shipping microservice will not be very different either. This will be responsible for checking if a shipment can be generated for the order and create one if possible.

As before we'll define a controller to expose our endpoints, in fact just a single endpoint:

@PostMapping public Order process(@RequestBody Order order) { return shippingService.handleOrder(order); }

and a service to encapsulate the business logic related to order shipment:

public Order handleOrder(Order order) { LocalDate shippingDate = null; if (LocalTime.now().isAfter(LocalTime.parse("10:00")) && LocalTime.now().isBefore(LocalTime.parse("18:00"))) { shippingDate = LocalDate.now().plusDays(1); } else { throw new RuntimeException("The current time is off the limits to place order."); } shipmentRepository.save(new Shipment() .setAddress(order.getShippingAddress()) .setShippingDate(shippingDate)); return order.setShippingDate(shippingDate) .setOrderStatus(OrderStatus.SUCCESS); }

Our simple shipping service is just checking the valid time window to place orders. We'll avoid discussing the rest of the boilerplate code as before.

4.5. Order Microservice

Finally, we'll define an order microservice which will be responsible for creating a new order apart from other things. Interestingly, it'll also play as an orchestrator service where it will communicate with the inventory service and the shipping service for the order.

Let's define our controller with the required endpoints:

@PostMapping public Order create(@RequestBody Order order) { Order processedOrder = orderService.createOrder(order); if (OrderStatus.FAILURE.equals(processedOrder.getOrderStatus())) { throw new RuntimeException("Order processing failed, please try again later."); } return processedOrder; } @GetMapping public List getAll() { return orderService.getOrders(); }

And, a service to encapsulate the business logic related to orders:

public Order createOrder(Order order) { boolean success = true; Order savedOrder = orderRepository.save(order); Order inventoryResponse = null; try { inventoryResponse = restTemplate.postForObject( inventoryServiceUrl, order, Order.class); } catch (Exception ex) { success = false; } Order shippingResponse = null; try { shippingResponse = restTemplate.postForObject( shippingServiceUrl, order, Order.class); } catch (Exception ex) { success = false; HttpEntity deleteRequest = new HttpEntity(order); ResponseEntity deleteResponse = restTemplate.exchange( inventoryServiceUrl, HttpMethod.DELETE, deleteRequest, Order.class); } if (success) { savedOrder.setOrderStatus(OrderStatus.SUCCESS); savedOrder.setShippingDate(shippingResponse.getShippingDate()); } else { savedOrder.setOrderStatus(OrderStatus.FAILURE); } return orderRepository.save(savedOrder); } public List getOrders() { return orderRepository.findAll(); }

The handling of orders where we're orchestrating calls to inventory and shipping services is far from ideal. Distributed transactions with multiple microservices is a complex topic in itself and beyond the scope of this tutorial.

However, we'll see later in this tutorial how a reactive system can avoid the need for distributed transactions to a certain extent.

As before, we'll not go through the rest of the boilerplate code. However, this can be referenced in the GitHub repo.

4.6. Front-end

Let's also add a user interface to make the discussion complete. The user interface will be based on Angular and will be a simple single-page application.

We'll need to create a simple component in Angular to handle create and fetch orders. Of specific importance is the part where we call our API to create the order:

createOrder() { let headers = new HttpHeaders({'Content-Type': 'application/json'}); let options = {headers: headers} this.http.post('//localhost:8080/api/orders', this.form.value, options) .subscribe( (response) => { this.response = response }, (error) => { this.error = error } ) }

The above code snippet expects order data to be captured in a form and available within the scope of the component. Angular offers fantastic support for creating simple to complex forms using reactive and template-driven forms.

Also important is the part where we get previously created orders:

getOrders() { this.previousOrders = this.http.get(''//localhost:8080/api/orders'') }

Please note that the Angular HTTP module is asynchronous in nature and hence returns RxJS Observables. We can handle the response in our view by passing them through an async pipe:

Your orders placed so far:

    
    
     
  • Order ID: {{ order.id }}, Order Status: {{order.orderStatus}}, Order Message: {{order.responseMessage}}
    •

Of course, Angular will require templates, styles, and configurations to work, but these can be referred to in the GitHub repository. Please note that we have bundled everything in a single component here, which is ideally not something we should do.

But, for this tutorial, those concerns are not in scope.

4.7. Deploying the Application

Now that we've created all individual parts of the application, how should we go about deploying them? Well, we can always do this manually. But we should be careful that it can soon become tedious.

For this tutorial, we'll use Docker Compose to build and deploy our application on a Docker Machine. This will require us to add a standard Dockerfile in each service and create a Docker Compose file for the entire application.

Let's see how this docker-compose.yml file looks:

version: '3' services: frontend: build: ./frontend ports: - "80:80" order-service: build: ./order-service ports: - "8080:8080" inventory-service: build: ./inventory-service ports: - "8081:8081" shipping-service: build: ./shipping-service ports: - "8082:8082"

This is a fairly standard definition of services in Docker Compose and does not require any special attention.

4.8. Problems With This Architecture

Now that we have a simple application in place with multiple services interacting with each other, we can discuss the problems in this architecture. There are what we'll try to address in the following sections and eventually get to the state where we would have transformed our application into a reactive system!

While this application is far from a production-grade software and there are several issues, we'll focus on the issues that pertain to the motivations for reactive systems:

  • Failure in either inventory service or shipping service can have a cascading effect
  • The calls to external systems and database are all blocking in nature
  • The deployment cannot handle failures and fluctuating loads automatically

5. Reactive Programming

Blocking calls in any program often result in critical resources just waiting for things to happen. These include database calls, calls to web services, and file system calls. If we can free up threads of execution from this waiting and provide a mechanism to circle back once results are available, it will yield much better resource utilization.

This is what adopting the reactive programming paradigm does for us. While it's possible to switch over to a reactive library for many of these calls, it may not be possible for everything. For us, fortunately, Spring makes it much easier to use reactive programming with MongoDB and REST APIs:

Spring Data Mongo has support for reactive access through the MongoDB Reactive Streams Java Driver. It provides ReactiveMongoTemplate and ReactiveMongoRepository, both of which have extensive mapping functionality.

Spring WebFlux provides the reactive-stack web framework for Spring, enabling non-blocking code and Reactive Streams backpressure. It leverages the Reactor as its reactive library. Further, it provides WebClient for performing HTTP requests with Reactive Streams backpressure. It uses Reactor Netty as the HTTP client library.

5.1. Inventory Service

We'll begin by changing our endpoints to emit reactive publishers:

@GetMapping public Flux getAllProducts() { return productService.getProducts(); }
@PostMapping public Mono processOrder(@RequestBody Order order) { return productService.handleOrder(order); } @DeleteMapping public Mono revertOrder(@RequestBody Order order) { return productService.revertOrder(order); }

Obviously, we'll have to make necessary changes to the service as well:

@Transactional public Mono handleOrder(Order order) { return Flux.fromIterable(order.getLineItems()) .flatMap(l -> productRepository.findById(l.getProductId())) .flatMap(p -> { int q = order.getLineItems().stream() .filter(l -> l.getProductId().equals(p.getId())) .findAny().get() .getQuantity(); if (p.getStock() >= q) { p.setStock(p.getStock() - q); return productRepository.save(p); } else { return Mono.error(new RuntimeException("Product is out of stock: " + p.getId())); } }) .then(Mono.just(order.setOrderStatus("SUCCESS"))); } @Transactional public Mono revertOrder(Order order) { return Flux.fromIterable(order.getLineItems()) .flatMap(l -> productRepository.findById(l.getProductId())) .flatMap(p -> { int q = order.getLineItems().stream() .filter(l -> l.getProductId().equals(p.getId())) .findAny().get() .getQuantity(); p.setStock(p.getStock() + q); return productRepository.save(p); }) .then(Mono.just(order.setOrderStatus("SUCCESS"))); }

5.2. Shipping Service

Similarly, we'll change the endpoint of our shipping service:

@PostMapping public Mono process(@RequestBody Order order) { return shippingService.handleOrder(order); }

And, corresponding changes in the service to leverage reactive programming:

public Mono handleOrder(Order order) { return Mono.just(order) .flatMap(o -> { LocalDate shippingDate = null; if (LocalTime.now().isAfter(LocalTime.parse("10:00")) && LocalTime.now().isBefore(LocalTime.parse("18:00"))) { shippingDate = LocalDate.now().plusDays(1); } else { return Mono.error(new RuntimeException("The current time is off the limits to place order.")); } return shipmentRepository.save(new Shipment() .setAddress(order.getShippingAddress()) .setShippingDate(shippingDate)); }) .map(s -> order.setShippingDate(s.getShippingDate()) .setOrderStatus(OrderStatus.SUCCESS)); }

5.3. Order Service

We'll have to make similar changes in the endpoints of the order service:

@PostMapping public Mono create(@RequestBody Order order) { return orderService.createOrder(order) .flatMap(o -> { if (OrderStatus.FAILURE.equals(o.getOrderStatus())) { return Mono.error(new RuntimeException("Order processing failed, please try again later. " + o.getResponseMessage())); } else { return Mono.just(o); } }); } @GetMapping public Flux getAll() { return orderService.getOrders(); }

The changes to service will be more involved as we'll have to make use of Spring WebClient to invoke the inventory and shipping reactive endpoints:

public Mono createOrder(Order order) { return Mono.just(order) .flatMap(orderRepository::save) .flatMap(o -> { return webClient.method(HttpMethod.POST) .uri(inventoryServiceUrl) .body(BodyInserters.fromValue(o)) .exchange(); }) .onErrorResume(err -> { return Mono.just(order.setOrderStatus(OrderStatus.FAILURE) .setResponseMessage(err.getMessage())); }) .flatMap(o -> { if (!OrderStatus.FAILURE.equals(o.getOrderStatus())) { return webClient.method(HttpMethod.POST) .uri(shippingServiceUrl) .body(BodyInserters.fromValue(o)) .exchange(); } else { return Mono.just(o); } }) .onErrorResume(err -> { return webClient.method(HttpMethod.POST) .uri(inventoryServiceUrl) .body(BodyInserters.fromValue(order)) .retrieve() .bodyToMono(Order.class) .map(o -> o.setOrderStatus(OrderStatus.FAILURE) .setResponseMessage(err.getMessage())); }) .map(o -> { if (!OrderStatus.FAILURE.equals(o.getOrderStatus())) { return order.setShippingDate(o.getShippingDate()) .setOrderStatus(OrderStatus.SUCCESS); } else { return order.setOrderStatus(OrderStatus.FAILURE) .setResponseMessage(o.getResponseMessage()); } }) .flatMap(orderRepository::save); } public Flux getOrders() { return orderRepository.findAll(); }

This kind of orchestration with reactive APIs is no easy exercise and often error-prone as well as hard to debug. We'll see how this can be simplified in the next section.

5.4. Front-end

Now, that our APIs are capable of streaming events as they occur, it's quite natural that we should be able to leverage that in our front-end as well. Fortunately, Angular supports EventSource, the interface for Server-Sent Events.

Let's see how can we pull and process all our previous orders as a stream of events:

getOrderStream() { return Observable.create((observer) => { let eventSource = new EventSource('//localhost:8080/api/orders') eventSource.onmessage = (event) => { let json = JSON.parse(event.data) this.orders.push(json) this._zone.run(() => { observer.next(this.orders) }) } eventSource.onerror = (error) => { if(eventSource.readyState === 0) { eventSource.close() this._zone.run(() => { observer.complete() }) } else { this._zone.run(() => { observer.error('EventSource error: ' + error) }) } } }) }

6. Message-Driven Architecture

The first problem we're going to address is related to service-to-service communication. Right now, these communications are synchronous, which presents several problems. These include cascading failures, complex orchestration, and distributed transactions to name a few.

An obvious way to solve this problem is to make these communications asynchronous. A message broker for facilitating all service-to-service communication can do the trick for us. We'll use Kafka as our message broker and Spring for Kafka to produce and consume messages:

We'll use a single topic to produce and consume order messages with different order statuses for services to react.

Let's see how each service needs to change.

6.1. Inventory Service

Let's begin by defining the message producer for our inventory service:

@Autowired private KafkaTemplate kafkaTemplate; public void sendMessage(Order order) { this.kafkaTemplate.send("orders", order); }

Next, we'll have to define a message consumer for inventory service to react to different messages on the topic:

@KafkaListener(topics = "orders", groupId = "inventory") public void consume(Order order) throws IOException { if (OrderStatus.RESERVE_INVENTORY.equals(order.getOrderStatus())) { productService.handleOrder(order) .doOnSuccess(o -> { orderProducer.sendMessage(order.setOrderStatus(OrderStatus.INVENTORY_SUCCESS)); }) .doOnError(e -> { orderProducer.sendMessage(order.setOrderStatus(OrderStatus.INVENTORY_FAILURE) .setResponseMessage(e.getMessage())); }).subscribe(); } else if (OrderStatus.REVERT_INVENTORY.equals(order.getOrderStatus())) { productService.revertOrder(order) .doOnSuccess(o -> { orderProducer.sendMessage(order.setOrderStatus(OrderStatus.INVENTORY_REVERT_SUCCESS)); }) .doOnError(e -> { orderProducer.sendMessage(order.setOrderStatus(OrderStatus.INVENTORY_REVERT_FAILURE) .setResponseMessage(e.getMessage())); }).subscribe(); } }

This also means that we can safely drop some of the redundant endpoints from our controller now. These changes are sufficient to achieve asynchronous communication in our application.

6.2. Shipping Service

The changes in shipping service are relatively similar to what we did earlier with the inventory service. The message producer is the same, and the message consumer is specific to shipping logic:

@KafkaListener(topics = "orders", groupId = "shipping") public void consume(Order order) throws IOException { if (OrderStatus.PREPARE_SHIPPING.equals(order.getOrderStatus())) { shippingService.handleOrder(order) .doOnSuccess(o -> { orderProducer.sendMessage(order.setOrderStatus(OrderStatus.SHIPPING_SUCCESS) .setShippingDate(o.getShippingDate())); }) .doOnError(e -> { orderProducer.sendMessage(order.setOrderStatus(OrderStatus.SHIPPING_FAILURE) .setResponseMessage(e.getMessage())); }).subscribe(); } }

We can safely drop all the endpoints in our controller now as we no longer need them.

6.3. Order Service

The changes in order service will be a little more involved as this is where we were doing all the orchestration earlier.

Nevertheless, the message producer remains unchanged, and message consumer takes on order service-specific logic:

@KafkaListener(topics = "orders", groupId = "orders") public void consume(Order order) throws IOException { if (OrderStatus.INITIATION_SUCCESS.equals(order.getOrderStatus())) { orderRepository.findById(order.getId()) .map(o -> { orderProducer.sendMessage(o.setOrderStatus(OrderStatus.RESERVE_INVENTORY)); return o.setOrderStatus(order.getOrderStatus()) .setResponseMessage(order.getResponseMessage()); }) .flatMap(orderRepository::save) .subscribe(); } else if ("INVENTORY-SUCCESS".equals(order.getOrderStatus())) { orderRepository.findById(order.getId()) .map(o -> { orderProducer.sendMessage(o.setOrderStatus(OrderStatus.PREPARE_SHIPPING)); return o.setOrderStatus(order.getOrderStatus()) .setResponseMessage(order.getResponseMessage()); }) .flatMap(orderRepository::save) .subscribe(); } else if ("SHIPPING-FAILURE".equals(order.getOrderStatus())) { orderRepository.findById(order.getId()) .map(o -> { orderProducer.sendMessage(o.setOrderStatus(OrderStatus.REVERT_INVENTORY)); return o.setOrderStatus(order.getOrderStatus()) .setResponseMessage(order.getResponseMessage()); }) .flatMap(orderRepository::save) .subscribe(); } else { orderRepository.findById(order.getId()) .map(o -> { return o.setOrderStatus(order.getOrderStatus()) .setResponseMessage(order.getResponseMessage()); }) .flatMap(orderRepository::save) .subscribe(); } }

The consumer here is merely reacting to order messages with different order statuses. This is what gives us the choreography between different services.

Lastly, our order service will also have to change to support this choreography:

public Mono createOrder(Order order) { return Mono.just(order) .flatMap(orderRepository::save) .map(o -> { orderProducer.sendMessage(o.setOrderStatus(OrderStatus.INITIATION_SUCCESS)); return o; }) .onErrorResume(err -> { return Mono.just(order.setOrderStatus(OrderStatus.FAILURE) .setResponseMessage(err.getMessage())); }) .flatMap(orderRepository::save); }

Note that this is far simpler than the service we had to write with reactive endpoints in the last section. Asynchronous choreography often results in far simpler code, although it does come at the cost of eventual consistency and complex debugging and monitoring. As we may guess, our front-end will no longer get the final status of the order immediately.

7. Container Orchestration Service

The last piece of the puzzle that we want to solve is related to deployment.

What we want in the application is ample redundancy and a tendency to scale up or down depending upon the need automatically.

We've already achieved containerization of services through Docker and are managing dependencies between them through Docker Compose. While these are fantastic tools in their own right, they do not help us to achieve what we want.

Hence, we need a container orchestration service that can take care of redundancy and scalability in our application. While there are several options, one of the popular ones includes Kubernetes. Kubernetes provides us with a cloud vendor-agnostic way to achieve highly scalable deployments of containerized workloads.

Kubernetes wraps containers like Docker into Pods, which are the smallest unit of deployment. Further, we can use Deployment to describe the desired state declaratively.

Deployment creates ReplicaSets, which internally is responsible for bringing up the pods. We can describe a minimum number of identical pods that should be running at any point in time. This provides redundancy and hence high availability.

Let's see how can we define a Kubernetes deployment for our applications:

apiVersion: apps/v1 kind: Deployment metadata: name: inventory-deployment spec: replicas: 3 selector: matchLabels: name: inventory-deployment template: metadata: labels: name: inventory-deployment spec: containers: - name: inventory image: inventory-service-async:latest ports: - containerPort: 8081 --- apiVersion: apps/v1 kind: Deployment metadata: name: shipping-deployment spec: replicas: 3 selector: matchLabels: name: shipping-deployment template: metadata: labels: name: shipping-deployment spec: containers: - name: shipping image: shipping-service-async:latest ports: - containerPort: 8082 --- apiVersion: apps/v1 kind: Deployment metadata: name: order-deployment spec: replicas: 3 selector: matchLabels: name: order-deployment template: metadata: labels: name: order-deployment spec: containers: - name: order image: order-service-async:latest ports: - containerPort: 8080

Here we're declaring our deployment to maintain three identical replicas of pods at any time. While this is a good way to add redundancy, it may not be sufficient for varying loads. Kubernetes provides another resource known as the Horizontal Pod Autoscaler which can scale the number of pods in a deployment based on observed metrics like CPU utilization.

Please note that we have just covered the scalability aspects of the application hosted on a Kubernetes cluster. This does not necessarily imply that the underlying cluster itself is scalable. Creating a high availability Kubernetes cluster is a non-trivial task and beyond the scope of this tutorial.

8. Resulting Reactive System

Now that we've made several improvements in our architecture, it's perhaps time to evaluate this against the definition of a Reactive System. We'll keep the evaluation against the four characteristics of a Reactive Systems we discussed earlier in the tutorial:

  • Responsive: The adoption of the reactive programming paradigm should help us achieve end-to-end non-blocking and hence a responsive application
  • Resilient: Kubernetes deployment with ReplicaSet of the desired number of pods should provide resilience against random failures
  • Elastic: Kubernetes cluster and resources should provide us the necessary support to be elastic in the face of unpredictable loads
  • Message-Driven: Having all service-to-service communication handled asynchronously through a Kafka broker should help us here

While this looks quite promising, it's far from over. To be honest, the quest for a truly reactive system should be a continuous exercise of improvements. We can never preempt all that can fail in a highly complex infrastructure, where our application is just a small part.

A reactive system thus will demand reliability from every part that makes the whole. Right from the physical network to infrastructure services like DNS, they all should fall in line to help us achieve the end goal.

Often, it may not be possible for us to manage and provide the necessary guarantees for all these parts. And this is where a managed cloud infrastructure helps alleviate our pain. We can choose from a host of services like IaaS (Infeastrure-as-a-Service), BaaS (Backend-as-a-Service), and PaaS (Platform-as-a-Service) to delegate the responsibilities to external parties. This leaves us with the responsibility of our application as far as possible.

9. Conclusion

In this tutorial, we went through the basics of reactive systems and how does it compare with reactive programming. We created a simple application with multiple microservices and highlighted the problems we intend to solve with a reactive system.

Además, seguimos adelante, presentando programación reactiva, arquitectura basada en mensajes y servicio de orquestación de contenedores en la arquitectura para realizar un sistema reactivo.

Por último, discutimos la arquitectura resultante y cómo sigue siendo un viaje hacia el sistema reactivo. Este tutorial no nos presenta todas las herramientas, marcos o patrones que pueden ayudarnos a crear un sistema reactivo, pero nos presenta el viaje.

Como de costumbre, el código fuente de este artículo se puede encontrar en GitHub.